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Full text of "Automotive welding control using a state variable model."



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4 TITLE fand Judllil.) 



Automatic Welding Control Using a State 
Variable Model 



S TYfJ Of REPORT ft PERIOD COVERFO 



THESIS 



0. PERFORMING ORG. HI^OOT NUMSCK 



7. AuTmORCx 



a. CONTRACT OR GRANT NUMBERf.j 



MOODY, WILLIAM V. 



I ped'OKuinj organization nauC anO ADDRESS 

Massachusetts Institute of Technology 
Cambridge, MA 



10. PROGRAM EL Em^nT, PROJECT TASK 
ARZa 6 WORK UNIT NUMBERS 



II CONTROLLING Of ICE NAME anO ADDRESS 

CODE 031 

NAVAL POSTGRADUATE SCHOOL 

MONTEREY, CALIFORNIA 93940 



12. REPORT DATE 

June 1979 



II. NUMBER OF PAGES 



73 



MONITORING AGENCY NAME * AOORESSrif rttttorwnt Irom Controlling Olllct) 



IS SECURITY CLASS, tot Inla report) 



UNCLASS 



iTT DECLASSIFICATION/ DOWNGRADING 
SCHEDULE 



l« DISTRIBUTION STATEMENT tot thl, R»porl) 



APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED 



17. DISTRIBUTION STATEMENT tot <*- mbmtrmel mrtlorod In Blook 20, II dltlaronl from* Report) 



l« SUPPLEMENTARY NOTES 



l» KEY WORDS (Contlnuo on tmrmrmm aid* II nmco»*mrr and Identity by block ALMtoar; 



Naval Engineering 
Welding Processes 
Modern Control Theory 



20 ABSTRACT (Continue on rovormo aid* It nacaaaary and Idontlty by block msmbmr) 



SEE REVERSE 



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DD I * 71 1473 EDITION OP 1 MOV «• IS OBSOLETE 

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(Ja/clas 



ABSTRACT 



Although automatic welders have existed commercially for at least the past ten 
years, they have proved inadequate to fulfill the needs of current shipbuilding, 
marine structure and major pipeline manufacturing contractors. The recent 
Alaskan pipeline construction program where hundreds of skilled manual welding 
craftsmen were sought out and sent to our most northern state is a good example. 
The development of a reliable and adaptable automatic welding process capable of 
rapidly producing good weldments is considered to be a primary requirement of 
today's industry. 

Presented here is a description of the process variables encountered in the Gas 
Tungsten Arc and the Gas Metal Arc Welding processes; the description emphasis 
is placed on the variable interdependence which occurs in these processes. From 
these variable relationships, a ninth order non-linear state variable description 
of the Gas Metal Arc process is developed using nine first order non-linear 
differential relations. Further definition of the exact nature of these rela- 
tions will permit the development of a second generation automatic welder which 
will be a dramatic improvement over existing machines. 

This work is believed to be the first attempt to apply modern control theory to 
welding. 



DD 1» U73 UAJtLAS 

/- / vl rviAo t\ -\ a ft r A i ' ' ' '■ L - -■■'-■'■ ■ ■ 



Approved for public release; 
distribution unlimited. 



AUTOMATIC WELDING CONTROL USING A STATE VARIABLE MODEL 

by 

WILLIAM VINCENT MOODY 

B.S. Ocean Engineering, U.S. Naval Academy 

(1972) 

Submitted in Partial Fulfillment 
of the Requirements for the 
Degree of 

MASTER OF SCIENCE IN OCEAN ENGINEERING 

and the Degree of 

MASTER OF SCIENCE IN MECHANICAL ENGINEERING 

at the 

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 

May 1979 

(c) William Vincent Moody 197 9 



DUDLEY KNOX LIDRARY 

NAVAL POSTGRADUATE SCHOOL 

MONTEREY, CALIF, 93940 



AUTOMATIC WELDING CONTROL USING A STATE VARIABLE MODEL 

by 

WILLIAM VINCENT MOODY 

Submitted to the Department of Ocean Engineering and the 
Department of Mechanical Engineering on May 11, 1979, in 
partial fulfillment of the requirements for the Degree of 
Master of Science in Ocean Engineering and the Degree of 
Master of Science in Mechanical Engineering. 

ABSTRACT 

Although automatic welders have existed commercially 
for at least the past ten years, they have proved inadequate 
to fulfill the needs of current shipbuilding, marine structure 
and major pipeline manufacturing contractors. The recent 
Alaskan pipeline construction program where hundreds of skilled 
manual welding craftsmen were sought out and sent to our most 
northern state is a good example. The development of a reliable 
and adaptable automatic welding process capable of rapidly pro- 
ducing good weldments is considered to be a primary requirement 
of today's industry. 

Presented here is a description of the process variables 
encountered in the Gas Tungsten Arc and the Gas Metal Arc 
Welding processes; the description emphasis is placed on the 
variable interdependence which occurs in these processes. 
From these variable relationships, a ninth order non-linear 
state variable description of the Gas Metal Arc process is 
developed using nine first order non-linear differential 
relations. Further definition of the exact nature of these 
relations will permit the development of a second generation 
automatic welder which will be a dramatic improvement over 
existing machines. 

This work is believed to be the first attempt to apply 
modern control theory to welding. 



Supervisor: Koichi Masubuchi 

Title: Professor of Ocean Engineering and Materials 
Science 

Supervisor: Henry Martyn Paynter 

Title: Professor of Mechanical Engineering 



ACKNOWLEDGEMENTS 

I would like to express my appreciation to Professor 
Koichi Masubuchi and Professor Henry Paynter for the instruc- 
tion, contributions and suggestions that I received both in 
the courses which they taught and in their support of this 
work. Without their guidance, this thesis would not have 
achieved its final form. 



The real purpose of scientific method is to 
make sure Nature hasn't misled you into thinking 
you know something you don't actually know. . . . 
If you get careless or go romanticizing scientific 
information, giving it a flourish here and there. 
Nature will soon make a complete fool out of you. 

— Robert M. Pirsig 
Zen and the Art of Motorcycle Maintenance 



TABLE OF CONTENTS 

Page 
Title Page 1 

Abstract 2 

Acknowledgements 3 

Table of Contents 5 

List of Figures 6 

List of Tables 8 

Sections 

I. INTRODUCTION 9 

II. WELDING PROCESS VARIABLES 14 

II. A. Control Variables 20 

II. A. 1. Arc Voltage and Current 2 

II. A. 2. Shielding Gas 27 

II. A. 3. Pulsing Shape and Frequency .... 28 

II. A. 4. Traverse Speed 31 

II. A. 5. Electrode Tracking Path 32 

1 1. A. 6. Welding Torch Movement 33 

II. A. 7. Wire Feed Rate 35 

II. A. 8. External Magnetic Fields 

Surrounding the Arc 3 5 

II. B. State Variables 36 

II.B.l. Puddle Shape and Size (Penetration) 36 

II. 3. 2. Metal Temperature Distribution. . . 36 

II. B. 3. Arc Temperature, Arc Shape 

and Composition 37 



5 



II. B. 4. Arc Length 38 

II. B. 5. Metal Transfer Mode (and Rate) ... 38 

II. B. 6. Arc Magnetic Field 43 

II. B. 7. Radiant Light Emission 49 

II. B. 8. Acoustic Emissions 49 

II. C. Summary 53 

III. STATE VARIABLE MODEL 55 

III. A. Simplifying Assumptions 55 

III.B. Model Variables 57 

III.C. Model Formulation 58 

IV. RECOMMENDATIONS AND CONCLUSIONS 64 

V. COMMENTS REGARDING AUTOMATIC WELDING 67 

REFERENCES 7 



6a 



LIST OF FIGURES 

Figure Title Page 

2-1 Block Diagram for Mechanized Gas Metal 

Arc Welding Systems 16 



2-2 Block Diagram for Mechanized Gas Tungsten 

Arc Welding Systems 17 



2-3 Drooping Voltage Characteristics in Welding 

Power Supplies 21 



2-4 Constant-Voltage and Increasing-Voltage 

Characteristics in Welding Power Supplies 23 



2-5 Constant-Current Characteristics in Welding 

Power Supplies 24 



2-5A Voltage Characteristics of a Welding Arc 25 



2-6 Gas Shield Configuration Developed at David 

W. Taylor Naval Research and Development Center 2 9 



2-7 Current Pulsing 31 



2-8 Possible Horizontal Electrode Tracking Path 

Positioning System 34 



2-9 Globular Transfer Mode 4 

2-10 Axial Spray Transfer Mode 41 

2-11 Short Circuit (or Dip) Transfer Mode 42 

2-12 Transition Current in GMA Welding 45 



2-13 Shift in Transition Current Magnitude with 

Material 4 6 



2-14 Shift in Transition Current Magnitude with 
Electrode Extension (Stick-out) 



2-15 Shift in Transition Current Magnitude with 
Electrode Diameter 



48 



2-16 Noise Emission Characteristics in Manual 

Stick Electrode Welding 51 



2-17 Possible Acoustic Emission Spectral Density 52 



3-1 State Variable Model for an Automatic GMA 

Welding Process 63 



LIST OF TABLES 

Table Title Page 

2-1 Control and State Variables in the GMA 

and GTA Welding Processes 15 



2-2 Shielding Gas Applications 27 



2-3 Fundamental Mechanisms of the GMA Metal 
Transfer Mode 



44 



I. INTRODUCTION 

The label "Automatic" has been attached to many 
different welding processes which have been developed by 
industry or discussed in technical literature over the last 
ten years; each of these automatic processes has attempted 
to reduce the decision making or manipulative functions 
played by the human welding operator by increasing the 
"intelligence" of the machine. However, the success of the 
above mentioned processes is dubious if the goal of auto- 
mation is to develop a truely versatile, adaptable and 
dependable welding process. The automatic machines currently 
in operation have narrow and extremely specific ranges of 
application. 

Two broad categories of automatic welders are employed 
in present industrial applications: "robot" welders and 
modified manual welders. Seemingly, each new issue of the 
popular welding journals contains at least one description 
of a new automatic machine which falls into one of the above 
categories. Automation systems of various stages of com- 
plexity have been designed and applied to welding processes 
such as Gas Metal Arc (GMA) , Gas Tungsten Arc (GTA) , Resis- 
tance, Electroslag and Submerged Arc. However, with the 
exception of certain assembly line type production processes 
such as the automobile industry, manual production welding 
completely dominates industry worldwide. One only has to look 
at current shipyard or pipeline production operations to 
verify this fact. 



Successful automation of welding processes is attrac- 
tive for one or more of the following reasons: 
Economic Considerations 

•Cheaper production costs 
•Reduced on-site manpower requirements 
•Reduced welder training costs 
•Increased production rates 
Weld Quality Improvement 

•Provide near ideal welding conditions 
•Weldment consistency and reproductiveness 
•Immediate post-welding non-destructive testing 
•Lower number of weld repairs 
To realize these substantial advantages over manual welding, 
a second generation automatic machine must be developed. 

Current automation systems employed on welders consist 
of some combination of open loop and closed loop control 
schemes. An open loop control scheme can be as simple as 
presetting the value of one process variable. The machine 
then attempts to match this preset value without any measured 
feedback. More advanced machines measure one or more of the 
variables in the welding process and, through the use of a 
controller, attempt to minimize variations of these variables 
from preset values. To illustrate the difference between an 
open loop and a closed loop control scheme, the variable 
weld head (electrode) position is a good example. In certain 



10 



pipeline manufacturing systems, the welding machine consists 
of multiple GMA welding heads which ride on a chain or steel 
band-type track that is attached circumf erentially around 
the pipe. This is an open loop form of control because the 
track is preset and invariant; the success of this scheme 
depends on how carefully the operators prepare the joint and 
allign the track. Closed loop control of the weld head 
( electrode ) position has been implemented on GMA machines 
designed to butt weld two flat plates. In this case, sensors 
such as a TV camera or a mechanical probe placed ahead of the 
electrode sense the track of the joint path and position the 
electrode via servomotor drives. By using feedback in this 
manner, joint irregularities can be overcome by the welding 
machine because the electrode tracking system can follow an 
imperfect joint matchup. 

Current automatic welders are successful only under 
specific applications. The major problem with these current 
machines is that only selected process variables are monitored 
or controlled. Because there are a relatively large number of 
variables in any welding process, the relevance to the final 
weldment quality of the variables selected for control is 
critical. For the most part, the controlled variables in 
current machines include arc length, arc voltage or current, 
current pulsing, wire feed rate, electrode tracking path, 
and electrode traverse speed. Therefore, the successful 

11 



machine designer has been forced to choose the right combina- 
tion of these variables to control in order to produce a 
successful machine. 

Recent advances in control theory are predicated on 
the ability of the control engineer to successfully model 
a process so that all pertinent process variables can be 
identified and measured (or at least estimated) ; then the 
process variables are controlled by minimizing any variation 
about a desired set point. To date modern control theory has 
not been applied to welding processes for a variety of rea- 
sons; the primary hurdle thus far has been the inability to 
develop a successful model. 

This work is the preliminary step of a multi-year 
project jointly headed by Professor Koichi Masubuchi, 
Professor Henry M. Paynter, and Mr. Frans Van Dyck at MIT 
to develop a fully automated, versatile and reliable welding 
process. This work will first identify a set of process 
variables taken from the Gas Metal Arc and Gas Tungsten Arc 
Welding processes. These processes were chosen because they 
appear to provide the broadest range of possible applications 
Secondly, each variable will be discussed thoroughly with the 
emphasis placed on the variable's suitability in a feedback 
control system. Finally, problems such as measurement, 
estimation, and processing of each variable will be 
discussed. 

12 



The ultimate goal of this work is to provide the 
basis for a model of the GMA (or GTA) process and then to be' 
gin to "close the loop" in the control formulation. Once 
this first step is completed, the project will continue to 
develop a variable controlled welding process. This com- 
pleted welding process, when coupled with immediate non- 
destructive weldment testing (NDT) and post-testing repair, 
will provide a rapid and reliable method for fabrication of 
such structures as U.S. Navy and commercial ships, pipeline 
systems, and large ocean platforms while incorporating the 
advantages listed earlier in this introduction. 



13 



II. WELDING PROCESS VARIABLES 

Unlike many systems and processes that are controlled 
today, welding presents a major problem because the desired 
output is not measurable. The goal of any welding process is 
to produce a weldment that matches the base metal in chemical, 
metallurgical and mechanical properties. However, weldment 
qualities such as porosity, amount of fusion, hardness, tough- 
ness, tensil strength, etc., cannot be determined "on-line" 
while the welding operation is in progress. Only after the 
process is completed can a weldment sample be taken and tested 
for its qualities. Therefore, other properties of the welding 
system must be monitored and from these properties, projections 
and estimations of the weldment qualities can be made. These 
weldment quality projections are based upon empirical rela- 
tionships that have been obtained from the millions of 
experiments completed by welding researchers. 

Using this empirical data base, weldment quality can be 
controlled by measuring or estimating and then controlling a 
set of process variables. In the following discussion, this 
set of process variables will be broken up into two subsets : the 
control (or manipulative) variables and the state variables. The 
control (manipulative) variables are defined as those variables 
that can be manipulated directly by the welding machine or the weld- 
ing operator. The state variables are defined as those variables 
which are determined by the welding process itself once the 
control (manipulative) variables have been set. The state variables 
cannot be manipulated directly but are functions of the welding process. 

14 



Figures 2-1 and 2-2 are block diagrams of the Gas Metal 
Arc and Gas Tungsten Arc welding processes. These diagrams 
identify the principal components of these systems. Through 
the use of these figures, the reader can gain an appreciation 
for the complexity of the welding processes and can identify 
how the following listing of state variables and control (manipulative) 
variables are related schematically. This list (Table 2-1) 
is meant to be as complete as possible and was developed from 
a similar list contained in Appendix C of reference 20. 



TABLE 2-1 
CONTROL AND STATE VARIABLES IN THE GMA AND GTA WELDING PROCESSES 



Control (manipulative) Variables 



State Variables 



Current 

Voltage (AC or DC and 
polarity) 

Pulsing Frequency and Shape 

Traverse Speed 

Electrode Tracking Path 

Welding Torch Movement 

Wire Feed Rate (GMA) 

Filler Wire Feed Rate (GTA) 

Composition and Flow Pattern 
of Protecting Gas 

External Magnetic Fields 
Surrounding Arc 

Coolant Flow 



Puddle Shape and Size 
(Penetration) 

Metal Temperature Distribution 

Arc Temperature 

Arc Shape and Composition 

Arc Length 

Metal Transfer Mode 

Arc Magnetic Field 

Acoustic Emission 

Radiant Light Emission 

Electrode Extension Length (GMA) 



15 



FIGURE 2-1 
BLOCK DIAGRAM FOR MECHANIZED 

GAS METAL ARC WELDING SYSTEMS 



Optional 
Welding Head 
Coolant System 


1 I 

j 1 
L . 





Electrical 

Power 

Supply 



\S 



Welding 

Head 

Tracking 

and 

Positioning 

System 



iL_xi 



5Z. 



Welding Head 
and Consummable 
Electrode 





Shielding 
Gas Feed 
System 



I—— — ■— | W^JM. ' .** ILUiU 



Wire Feed 
System 



Power Losses 
Thermal 

Emissions 
Acoustic 

Emissions 
Radiant Light 

Emissions 
Instabilities 
Magnetic 

Effects 
Other Losses 



16 



FIGURE 2-2 
BLOCK DIAGRAM FOR MECHANIZED 
GAS TUNGSTEN ARC WELDING SYSTEMS 



Electrical 

Power 
Supply 









; 



Optional 
Filler 
Wire Feed 



>_-_-rrr{> 



Welding Head 
Tracking and 
Positioning 
System 



>*»■ i «i ■■-»■»*«■ 



Optional 
Welding Head 
Coolant System 



^> S? 



Welding Head 
and Non- 
Consummable 
Electrode 



'±Z. 



Arc 



\7 



Weldment 



«= 




j 



Shielding Gas 
Feed System 



Power Losses 


Thermal Emissions 


Acoustic 


Emissions 


Radiant Light 


Emissions 


Instabilities 


Magnetic Effects 


Other Losses 



17 



To define a control scheme for these processes, first 
a brief discussion of nomenclature is required. Initially a 
simplified diagram will be proposed which will later be ex- 
panded into a final state variable model. The control vector 
U will be an m-component vector containing part or all of the 
above listed control variables. The state vector X will be 
defined as an n-component vector containing part or all of 
the above listed state variables. The final choice of the 
components of the control and state variable vectors should 
be the minimum set of variables that accurately describe all 
of the important functions of the welding process. 

These vectors can be mathematically represented as 
follows : 



U = vector of m control 

(manipulative) variables 



U. 



U. 



u 



m-1 



U_ 



m 



J 



18 



X = vector of n state variables = 



X. 



X, 



X 



n-1 



X 

L n J 



2- at 5 



Then the relationship between X and U can be diagrammed as 




where F(X, U, t) is a set of time dependent, non-linear 
equations describing the welding process. By adding a 
controller to this representation, a closed loop process 
is obtained. 



19 



ri 



U 






X = F(X, U, t) 



"1 






Controller 



* 



For the remainder of Section II, each control and 

t 

state variable will be discussed in detail. 



II. A. 



Control Variables 



II. A. 1. Arc Voltage and Current 

The magnitude of voltage and current supplied by 
the power supply in the welding process is directly related 
to the amount of heat input that is supplied to the weldment 
once power losses are taken into consideration. Power 
supplies currently used in the GTA process have a "drooping" 
characteristic which supply a maximum open circuit voltage 
and then decrease to a zero value of short circuit voltage 
(Figure 2-3). Power supplies used in GMA processes have 
constant or increasing voltage characteristics (Figure 2-4) . 

20 



FIGURE 2-3: Drooping Voltage Characteristics in Welding 
Power Supplies 



Voltage 




Current 
DROOPING CHARACTERISTIC 



L = arc length 



L 2 > L x 



P = open circuit voltage 
T = short circuit voltage 



(Masubuchi, 18, p. 2-46) 



21 



The constant voltage power supply allows the GMA 
process to self-regulate arc length because weld current, 
arc voltage and arc length are interrelated in the following 
way: 

1. Arc length vairation in the GMA process is 
minimized if the melting rate (or burn-off rate) 
of the consummable electrode (inches per second) 
is equal to the feed rate of the electrode. 

2. Arc length is directly proportional to arc voltage — 
increasing arc length increases arc voltage. 

3. Melting rate increases with current increase. 
For example, if the wire-feed speed increases, the arc 
length and arc voltage decreases, causing an increase in 
current. 1 This increase in current increases the melting 
rate and burns-off the electrode more quickly; therefore the 
arc length increases (Masubuchi, 18, p. 2-49). Schaper, in 
reference 30, has discovered experimentally that welding 
performance in the GMA process is further improved if the 
power supply is capable of providing both the characteristics 
of constant voltage and constant current (Figure 2-5) by 
operating over the entire volt-ampere range; an example of 
such a machine is the TEK-TRAN linear slope control power 
supply type LSC 750. 

For the majority of applications in both the GMA 
and GTA processes, direct current is used. Alternating 
current has limited, specific applications such as the 

22 



FIGURE 2-4: Constant-Voltage and Increasing Voltage 

Characteristics in Welding Power Supplies 





Current Current 

a. CONSTANT-VOLTAGE CHARACTERISTIC b. INCREASING "VOLTAGE CHARACTERISTIC 



L = arc length 



L 2 > L x 



P = open circuit voltage 

Q = unstable arc operating point 

S = stable arc operating point 



(Masubuchi, 18, p. 2-48) 



23 



FIGURE 2-5: Constant Current Characteristics in Welding 
Power Supplies 



Voltage 




Current 
CONSTANT-CURRENT CHARACTERISTIC 



i\j 



L = arc length 



L 2 > L 



p = open circuit voltage 

T = short circuit voltage constant current value 



(Masubuchi, 18, p. 2-46) 



24 



FIGURE 2-5A: Voltage Characteristics of a Welding Arc 



(o) 



I 



II 



A/>A 



D.C. POWER SUPPLY RESISTANCE 2! 

DC 

U 



CATHODct. . •:.;>'.. • 



(b) 



UJ 

o 

< 

o 

> 

o 
or 
< 

t 



ANODE , 

V:V^:;;:;;'./.-,;Vi^s^ f 



ARC COLUMN 

I 




'V 

V A (ANOOE VOLTAGE DROP) 

--■}- 

VplARC COLUMN VOLTAGE DROP) 

-1- 

V c (CATHODE VOLTAGE DROP) 



(Masubuchi, 18, p. 2-6! 



25 



application of high frequency in the GTA process to improve 
the arc stability. In direct current applications, two 
choices of polarity are available and are labeled Direct 
Current Straight Polarity (DCSP) and Direct Current Reverse 
Polarity (DCRP) . 



Electrode 



G 



Cathode 



© [ 



Anode 



Base Plate 



Electrode 



d 



Anode 



J 







Cathode 



Base Plate 



Direct Current Straight 
Polarity 



Direct Current Reverse 
Polarity 



J 



DCSP is used in the majority of GTA welding applications 
because approximately 80% of the heat is developed at the 
Anode in a welding arc and 5% of the heat is developed at 
the Cathode. Primarily, the use of DCRP in GTA welding 
occurs when Aluminum or Magnesium are welded; the oxide 
coating on these metals is removed much more readily when 
using DCRP by an unknown mechanism (possibly the positive 
ions striking the plate) (Masubuchi, 18) . 



26 



Conversely, DCRP is the most common polarity used in 
GMA welding. DCRP provides a faster melting rate of the 
electrode and, consequently, a higher metal transfer rate. 



II. A. 2. Shielding Gas 

The primary consideration regarding the shielding 
gas is the correct choice of the right gas mixture; also, 
these gas mixtures must be free of impurities because any 
gas impurities can cause arc instabilities. These gas mix- 
tures have been determined experimentally for different 
applications and are summarized in the table below: 







SHIELDING 


TABLE 2-2 
GAS APPLICATIONS 






GTA 








GMA 




Base Metal 
Steels 


Gas 

Argon 
Helium 


Polarity 
DCSP 


■ ■ i 

1 


Base Metal Gas 

Stainless Argon 
Steel + 1% 0„ 


Polarity 

i 
DCRP 


Aluminum 
Magnesium 


Argon 
Helium 


DCRP 






Carbon C0~ or 


DCRP 



Titanium Argon DCSP 
Helium 



Steel 



co 2 +o 2 , 

Argon + 
0„ 



Alloy 
Steel 



Argon ■ 

o 2 , co 2 , 

or C0 2 +0 2 



DCRP 



Quenched & Argon + DCRP 
Tempered CO 
Steel l 



Aluminum 



Argon 
Helium 



Copper 



Argon 
Helium 



DCRP 
DCRP 



27 



The purpose of the shielding gas is to provide a 
shield from atmospheric contamination which causes porosity, 
embrittlement, cracking, and other problems. Schaper 
(reference 30) has developed a gas shield configuration 
which has significantly increased the ability of the 
shielding gasses to exclude the atmosphere from the weldment. 
This new shield has proved capable of dispersing the gas 
thoroughly and is diagrammed in Figure 2-6. 

Potential dynamic uses of the shielding gas have been 
discussed but not confirmed experimentally. For example, 
it may be possible to control the temperature on the outside 
arc boundary or even to position the arc by directing the 
flow of the gas. However, these dynamic uses may be invalid 
for outdoor applications on windy days even if they are 
developed. 

II. A. 3. Pulsing Shape and Frequency 

Current pulsing has applications in both GTA 
(Troyer, 33) and GMA (Lesnewich, 17) welding processes; the 
effects of pulsing for welding control are substantial and to 
date have not been completely documented. For example, in 
the GTA process pulsing can be used advantageously to agitate 
the weldment puddle to improve fusion and prevent cracking. 
However, the desirability of pulsed current is best understood 
by examining its effect in GMA applications. 



28 



FIGURE 2-6: Gas Shield Configuration Developed at David W. 
Taylor Naval Research and Development Center 



GAS INLET 




1/16" copper sum 



7 



o 



^ 



■SMtfio and surreal positions 

-ASBESTOS INSULATION 



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ElEV VICW 



TORCH OP.NING- 



WATER 



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- WAT I RUNE 
/A" COPPFR TUBF 



GAS„„ 
INLET 



COPPER 
MCSH- 



WAJEk' IN-^Cl 



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-jj.jui iliMiliiJ :ji;ili!)ji!!;i ijn r rrnrti m n /,.' 



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GAS I INE -VENTED 
3/l6"COPPLK U-IU 



SECTION A A 




1/16" COPPFR SHEET 
MOUSING 

-WATER LINE 




STAINIESS 
STEEL WOOL 



D 

LL-Lr . . j.--i 
SECTION B B 



1 1/2" 



(Schaper, 30, p. 6) 



29 



The metal transfer mode in GMA is determined by the 
magnitude of the applied current. To achieve spray transfer 
(metal transfer modes will be discussed later) , the magnitude 
of the current must reach a certain level. However, once 
this transition current value is reached, the metal transfer 
rate and the depth of penetration increase tremendously and 
are difficult to control. Therefore, to maintain an average 
low applied power and to control the metal transfer rate, 
current pulsing is used. Pulsed-power supplies provide a 
low average current but at the peak of the square wave pulse 
exceed the transition current (Figure 2-7) . Consequently, 
the metal transfer rate and depth of penetration can be 
controlled by choosing the frequency and magnitude of the 
pulses. 

Although pulsing is a promising variable with which 
to control both the GMA and GTA welding processes, much more 
developmental work must be completed. Currently, the magni- 
tude and shape (square wave) of the pulses are pre-programmed 
into the machine. However, once a larger empirical data base 
is obtained, active frequency and pulse shape variations may 
be implemented as part of a closed loop control scheme. 



30 



FIGURE 2-7 
CURRENT PULSING 



Current 
C„ 






r^^ 



s 



=1 



\j Transition 
-S^N+- _ _ — 

Current 



Time 



(Lesnewich, 17, p. 4) 



II-. A. 4 Traverse Speed 

Electrode traverse speed is inversely proportional 
to the amount of heat energy applied to the weldment. As the 
traverse speed decreases, the heat input increases. Most current 
machines implement a constant traverse speed. However, by 

measuring variables such as weldment puddle width and metal 
temperature distribution, traverse speed can be controlled 

and become a useful variable. 



31 



II. A. 5. Electrode Tracking Path 

To reduce joint preparation costs and to mini- 
mize joint allignment problems, some form of closed-loop 
tracking system should be incorporated as was discussed 
in the introduction of this study. Evans (reference 7) 
has performed a fairly extensive survey of electrode 
tracking systems in use through 1974. Generally, these 
systems are equipped with either mechanical or optical 
sensors which follow the joint. For example, the commerci- 
ally available "Arc-Tender" described by Evans uses a mech- 
anical probe that is inserted into the joint ahead of the 
torch and "feels" the path much like a blind person might 
use a cane. 

Optical photocell sensors have also been incorporated 
which also have the added advantage of sensing and controlling 
arc length (Evans, 7, p. 18). However, the two most promising 
methods of sensing the joint path appear to be either a 
television (or video scan) camera or the Cecil mechanical 
cross-slide tracking and sensoring system. 

The Cecil system has been used successfully in ship- 
yards and has most recently been used by Schaper for a 
narrow gap welder that is being developed at the David W. 
Taylor Research and Development Center. The system consists 
of a transducer controlled finger probe which is placed 
about 1/4 inch ahead of the torch and senses irregularities 



32 



in the joint sidewall. It can operate in joint thicknesses 
from to 2 inches (Schaper, 30, p. 6) . 

The television tracking system is attractive because 
it would not only provide joint path information but could 
also provide information regarding the weldment puddle width. 
In case of either the mechanical, transducer or television 
sensor, joint irregularities would be used to position the 
electrode via some form of servomotor drive. Probably the 
most useful configuration for the tracking system would in- 
volve mounting the torch on a carriage drive that follows a 
preset track; this track could be horizontally or vertically 
attached in the case of plate welding or could be circum- 
ferentially attached in the case of pipeline or cylindrical 
welding (Figure 2-8) . The servomotor positioning drive could 
then have the capability of positioning the torch ±2 inches 
(or as much as required) from the centerline of the preset 
track. 

II. A. 6. Welding Torch Movement 

The tracking system described above would give 
the torch two degrees of freedom. Additional degrees of 
freedom added to the torch would allow it to more closely 
duplicate the motions used in manual welding. Using the 
terms of Naval Architecture, the tracking system provides 
control of surge (electrode traverse speed) and sway 



33 



FIGURE 2-8: POSSIBLE HORIZONTAL ELECTRODE PATH POSITIONING 

SYSTEM 



Electric Power emdOGas Feed 



Sway 
Positioner 




Traverse Drive 
Unit 



/ / 

Fixed Track 



34 



(servomotor positioning) . Additional controls of heave 
(vertical) , roll (angular rotation about the axis parallel 
to the joint path) , and pitch (angular rotation about the 
axis parallel to the sway axis) may be useful. 

II. A. 7. Wire Feed Rate 

In most GMA applications, wire feed rate is preset and 
invariant. However, wire feed rate could be made variable 
and would be useful for controlling arc length. Experiments 
are needed in this area. 

II. A. 8. External Magnetic Fields Surrounding the Arc 

Jose Converti has suggested a form of magnetic 
arc control that he has begun to test experimentally using 
the GTA process (Converti, 13). By positioning 4 magnetic 
sensors at 90° intervals circumferentially about the arc, 
arc position and rate of change of arc position can be 
detected. Then, through the use of a set of deflection coils, 
the arc can be positioned in the joint. Possible other 
advantages of this scheme may be the control of arc shape 
and the control of arc instabilities. Dynamic control of 
the arc in this manner may have some useful consequences. 



35 



II. B. State Variables 

II.B.l. Puddle Shape and Size (Penetration) 

Vroman and Brandt (reference 34) have shown that 
the weldment puddle width can be measured using a video scan 
camera and this measurement can be used to control the torch 
traverse speed. The puddle width was preset into the controller 
and the welding process attempted to maintain this width 
throughout the length of the weldment. Good results were 
obtained from this experiment with the weldments displaying 
a high degree of uniformity. 

Vroman and Brandt suggested that this puddle width 
information also be used to manipulate the welding current. 
Limitations on the use of this variable are based primarily 
upon the accuracy and resolution capability of the video scan 
camera. However, puddle width is a good indication of the 
amount of heat input to the weldment and the metal deposition 
rate. 

II. B. 2. Metal Temperature Distribution 

By employing a temperature sensor such as a 
thermocouple at the front and back side of the weldment, 
indications about depth of penetration and heat input can 
be obtained. Bennett (reference 3) and Smith (reference 32) 
have indicated that this is a viable means of control. Some 
practical implementation considerations exist however; 



36 



for example, access restrictions to the back of the weldment 
are encourtered in pipeline welding or in the repair of 
large, high pressure steam lines. However, a NASA develop- 
ment in 1967 of a "hybird" thermocouple system for GTA 
aluminum welding proved that useful information could be 
obtained from the torch side of the joint only (Evans, 7, 
p. 18). The hybird thermocouples consisting of a constantan 
wire which contacted the aluminum near the arc. Holding the 

voltage constant, either the torch travel speed or the current 
were varied to maintain a constant temperature. The following 
conclusions were made from this NASA project: 

•Thermocouple insensitive to radiated arc 

interference and variations in surface emissivity. 
•Surface oxides produced no interference. 
•More successful for aluminum plate thicknesses 
of 1/8 inch or less. 
•No observable lag in response time. 

II. B. 3. Arc Temperature, Arc Shape and Composition 

Arc temperature can also be detected by some form 
of micro- thermistor or micro-pyrometer . However, many factors 
affect the arc and much more study regarding arc physics is 
required. For example, the principal arc temperature is 
determined by the choice of the type of shielding gas. The 
ionization potential for Helium (24.5 eV) is higher than the 

37 



ionization potential for Argon (15.7 eV) ; therefore the 
Helium arc temperature is significantly higher than the 
Argon arc temperature. The arc dynamics probably hold much 
useful information for the welding engineer but the physical 
mechanism is not fully understood at this time. 

II. B. 4. Arc Length 

Arc length is a critical variable in the welding 
process and must be controlled precisely. As stated previously, 
arc length is proportional to arc voltage and may also be 
regulated by the wire feed rate. Other information regarding 
arc length may be contained in the arc temperature emissions, 
light emissions and acoustic emissions ;, the acoustic emission 
relationship will be discussed below. 

II. B. 5. Metal Transfer Mode (and Rate) 

The metal transfer mode in the GMA process can 
take one of three forms: globular, spray (axial or rotating), 
or short-circuit (dip) . The transfer mode is a complicated 
phenomenum and is a function of many factors such as: 

•Shielding gas type 

•Current (I) 

•Polarity (DCRP, DCSP) 

•Electrode diameter (d) 

•Electrode extension or stick-out (L) 



38 



•Electrode metal type (steel, Al, etc.) 
•Activation of the electrode with alkali, alkaline 
earth and rare earth materials (Masubuchi, 18) 

The Globular mode (Figure 2-9) involves the transfer 
of large volume drops of metal (typically larger in diameter 
than the electrode) from the electrode to the weldment at 
the rate of 3 or 4 per second. These drops travel at a 
relatively low velocity. The Axial spray mode is the most 
desirable for most applications and consists of many small 
volume metal droplets that are accelerated to high velocities 
in a direction parallel to the length of the electrode. This 
mode deposits metal directly into weldment; it is the most 
efficient and stable mode. When the welding current exceeds 
a certain value, the electrode tip begins to rotate and 
sprays the metal at extremely high velocities in a cornical- 
type pattern. This rotating spray mode is highly unstable 
and should be avoided. 

For certain applications, short-circuit or dip transfer 
is desirable. For example, dip transfer can be used to 
increase the depth of penetration when welding steel. In dip 
transfer, a large metal globe is formed on the end of the 
electrode which simultaneously contacts the weldment; this 
globe then separates from the electrode and is accelerated 
into the weldment (Figure 2-11) . 



39 



FIGURE 2-9: Globular Transfer Mode 




Electrode 



Base Plate 



40 



FIGURE 2-10: Axial Spray Transfer Mode 




ectrode 



Base Plate 



41 



FIGURE 2-11: Short Circuit (or Dip) Transfer Mode 



\tlltJ/ ^ILLII/ 




J\ 



^M 





^uiuy 



At CD 




\ X 




,. Change of Electrode Position 



i A 




TIM1 (SICOMOftJ 



b. Current v? Tin* 



§ 30 



s i»h 



? o 




^ -* 



* «<• 






j 



01 .0* 

TIMI (SICCNOt) 



c. Voltage vi Tim« 



(Masubuchi, 18, p. 3-29) 



42 



The amelioration of the transfer mode from globular 
to axial spray occurs at a value of current known as the 
transition current. At this value of current, the drop 
volume decreases significantly and the drop velocity increases 
(Figure 2-12) . The value of current required in the transition 
region varies with the electrode material, the electrode 
diameter and the electrode extension. The electrode extension 
length determines how much resistance heating occurs as the 
electric power is conducted through the electrode; as the 
length increases, the resistance increases and the resistance 
heating increases. As the conductivity of the material 
increases, extension length becomes less of a factor. From 
a qualitative viewpoint, the transition current magnitude 
decreases as extension length increases and increases as 
electrode diameter increases (see Figures 2-13, 2-14, and 2-15). 

The metal transfer mode is a very critical variable 
in GMA welding and should be one of the primary considerations 
in any control scheme. Table 2-3 summarizes the important 
parameters affecting this variable. 

II. B. 6. Arc Magnetic Field 

As discussed previously in the control variable 

section, the magnetic characteristics of the arc can be 

sensed and maybe used to deflect the arc. Other information 

may be obtained from this variable but extensive experiments 

must be completed first. 

43 



TABLE 2-3 



FUNDAMENTAL MECHANISMS OF THE GMA METAL TRANSFER MODE 



TYPE OF TRANSFER MATERIAL POLARITY 



SHIELDING GAS 



Globular 



DCSP 
DCRP 



Helium, Argon 
C0„ 



Spray 



Steel 



DCRP 

DCSP with 
activated 
electrode 



Argon only 
Argon 



Dip 



Steel 



DCRP 



CO. 



44 



FIGURE 2-12: Transition Current in GMA Welding 



300 



1 „-^ l" 




■ ie o 



I 

s 

> 



a. 
o 



100 



200 MO «C0 



(Masubuchi, 18, p. 3-17) 



45 



FIGURE 2-13: Shift in Transition Current Magnitude with 
Material 




Current 



46 



FIGURE 2-14: Shift in Transition Current Magnitude with 
Electrode Extension (Stick-out) 



0) 

■0 
id 

« 

u 

0) 
4-1 
W 

c 

U 

Eh 



fd 
+J 

S 




Current 



£ = electrode extension 



*1 > £ 2 



47 



FIGURE 2-15: Shift in Transition Current Magnitude with 
Electrode Diameter 



0) 

+J 

OS 

s-i 

0) 

w 
c 
(0 
u 



<d 
+j 

<u 

s 




Current 



electrode diameter 



d 2 > dl 



48 



II. B. 7. Radiant Light Emission 

The light emission spectrum is one of the few 
variables listed in this section that is used as feedback 
by the manual welder. The brightness and intensity of the 
light is of sufficient magnitude that rapid damage occurs in 
the welder's eye unless protective shielding is worn. How- 
ever, the ability of the welder to process anything other 
than "brightness" and arc length information from the light 
emissions is debatable. With the use of more sophisticated 
equipment, spectral information may be recoverable from the 
light. Experiments have been performed in this area and a 
patent titled "Control System Using Radiant-Energy Detection 
Scanning" (US Patent Number 3,370,151 of 20 February 1968) 
has been issued to Neil J. Normando (Vroman, 34, p. 749). 
Glickstein (reference 9) also lists two references pertaining 
to this topic. From the results of further experimental work, 
light emissions could prove to be a useful variable. 

II. B. 8. Acoustic Emissions 

When observing a welding craftsman at work, one is 
amazed by what appears to be a lack of sensory feedback. 
Anyone who has attempted to initiate a shielded metal elec- 
trode arc while wearing the protective shield can appreciate 
the lack of visual feedback. However, the experienced 
welder still produces good welds. 

49 



Apparently, one of the feedback mechanisms that the 
welder uses is the crackling sound of a "good" weld. Welding 
shop foremen are able to monitor the progress of their pro- 
duction welders by listening to the sound; the sound has been 
described as the same sound bacon makes when frying in a pan. 

In a short series of experiments conducted with Mr. 
Anthony J. Zona at the MIT Welding Lab, a correlation between 
the noise emissions and the arc length was observed. By 
assuming the noise emissions to be band limited white noise, 
the following figure (Figure 2-16) approximates the nature of 
the acoustic output during welding. These characteristics 
are based upon observations made during manual stick electrode 
welding of steel plate. 

From these observations, the following conclusions 
were drawn: 

•The central, or dominant, frequency (f ) shifted 
to a lower value as the arc length increased. 

•As the arc length decreased, the frequency 
shifted to a higher value. 

•Acoustic energy decreased as the arc length 
increased and increased slightly as the arc 
length decreased. 

Although the acoustic energy is probably dependent 
upon many factors, metal transfer rate appears to be an 
important consideration. 

50 



FIGURE 2-16 

Noise Emission Characteristics in Manual Stick Electrode 

Welding 



w N (f) 

(White Noise 
assumed) 



Arc Length 
too long 

2 



Correct 
Arc Length 
2 



Arc Length 
too short 




frequency- 



Actual spectral characteristics of the acoustic 
spectrum would have a more general shape; hopefully, the 
spectral density curve will have the appearance of Figure 2-17 
Second order affects may be present in the form of peaks in 
the curve, but the curve as shown in Figure 2-17 has a 
dominant peak at f and then decays with frequency. 



51 



FIGURE 2-17 
Possible Acoustic Emission Spectral Density 



w N (f) 




frequency 



By attaching a directional transducer to the torch, 
these noise emissions could be measured and sent to a micro- 
processor. If the mean square value of the noise is the pri- 
mary important parameter in these emissions, the signal could 
be processed quite easily. However, if the frequency distri- 
bution of the noise is important (observations indicate it is 
important) , spectral density processing techniques would be 

required. 

52 



The instruments described in reference 1 have the 
capability of performing real time spectral density analysis 
over a band width of about five octaves. They have a sampling 
frequency of about 60,000 Hz and cost about $30,000 (1978 US) 
which may be fairly excessive for welding applications. How- 
ever, if the noise is narrow banded about the peak frequency 
f , perhaps a cheaper instrument with a narrower band width 
could be fabricated. 

If Figure 2-17 is the general appearance of the noise 
spectrum, optimal values of f would need to be obtained 
experimentally for each welding application. For example, 
according to Mr. Zona, the noise characteristics seem to 
change when welding in wet weather. This is a very compli- 
. cated area and will require a lot of developmental work. 

II. C. Summary 

The variable descriptions in this section are designed 
to give the reader an appreciation for the complexity of the 
welding process; particular emphasis has been placed on the 
interactions that occur between the variables. Only through 
an understanding of this variable interaction can the control 
engineer begin to develop a model and a control scheme for 
welding. The references contained at the end of this work 
provide a more complete description of the fundamentals of 
welding; in particular, reference 18 discusses in-depth many 
of the variables that have been presented here. 

53 



Although an effort has been made to identify every 
potential welding variable that is important to an automatic 
welder, current knowledge limitations restrict the use of 
some of them in a model of the process. In the following 
section/ an automatic welder model will be developed which 
encorporates ten of the variables listed in this section. 



54 



III. STATE VARIABLE MODEL 

In this section, a state variable model will be 
developed to describe the GMA welding process. The GMA 
process is considered to have more applications in major 
ocean structural production and is therefore more important; 
however, a similar model could be envisioned for the GTA 
process. 

III. A. Simplifying Assumptions 

Because the total number of variables described in 
Section II is enormous, certain simplifying assumptions must 
be made to produce a meaningful model. These assumptions are 
based on one or both of the following: 

•Lack of sufficient data or knowledge regarding 
the variable and its affect on the overall 
welding process. 

•The procedure required to control the variable 
is well defined currently and this variable can 
be added to the model easily. 
For the above reasons the following variables have been 
neglected from consideration in the final model: 
•Electrode Tracking Path 
•Welding Torch Movement 
•Shielding Gas 
•Pulsing Shape 
•Noise Emissions 

55 



•External Magnetic Fields 

•Arc Temperature 

•Arc Shape and Composition 

•Arc Magnetic Field 

•Radiant Light Emission 
The electrode tracking path can be controlled using 
existing technology and has no affect on the model once the 
assumption is made that the electrode remains in the joint. 
The major problem that will be encountered in controlling the 
movement along the joint path by the electrode is the choice 
of a suitable sensor that produces accurate and rapid position 
information. 

Likewise, welding torch movement and manipulation can 
be implemented once the movement requirements have been 
defined. For the following model, the torch will have one 
degree of freedom — forward transversing or surge. Sway 
(sideways) motion is assumed but only in conjunction with 
the electrode remaining in the joint path. 

For the remainder of the excluded variables listed 
above, not enough knowledge is currently available to determine 
the affect of implementing them in the control scheme. As 
further research is completed, these variables may be added 
to the model . 



56 



Finally, the model assumes that the type and diameter 
of the electrode have been chosen, that the shielding gas 
(Argon) has been chosen and that the material being welded 
has been chosen. Also, Direct Current Reverse Polarity is 
assumed. 

III.B. Model Variables 

The remaining variables are listed below along with 

a mathematical symbol which will be used in the model 

development. These variables are: 

Control (or Manipulative) Variables 
Variable Symbol 

•Current I 

•Voltage V 

•Traverse speed v 

•Pulsing frequency co 

•Wire feed rate C 

State Variables 

•Puddle (weldment) width w 

•Metal temperature T 

•Arc length L_ 

• Electrode Extension Length L 

These variables are assumed to be deterministic and 

measurable. Power supply characteristics are assumed to be 

known . 

57 



III.C. Model Formulation 

Taking the variables as defined in Section IILB. , the 
following vectors are identified. 



U = Control (or Manipulative) Variable Vector = 



V 



oo 



n 



X = State Variable Vector 



R = Reference vector (initial set points 
of the control vector) 



w 



i T 



M 



! "i 1 

o 



Y = Output vector = 



i OJ 

po 



i 



M 



T 

L A I 



! L T 



m 



58 



The output vector contains the four components of the 
state vector plus m which is the metal transfer rate; m is 
defined as: 



m = C,,(I,u> /L_) (3.1) 

5 p E 



Now by taking two equations proposed by Professor 
Masubuchi in his 13.17J course at MIT, the definition of the 
nature of the state relations can begin. These equations 
are as follows: 



B l 
V = A 1 L A + T X L A + C l (3 ' 2) 

(Masubuchi, 18, p. 2-31) 



M_ = A„I + B L„I 2 (3.3) 



r 2 2 E' 



(Masubuchi, 18, p. 3-52) 



A,, B, , C, , Ay and 3- are constants. The symbol d is the 
electrode diameter and the symbol M is the melting rate. 
Then these relationships may be established: 



ft (L E ) = ^E = F 4 ( meltin g rate, to, C) (3.4) 



59 



j£-(L fl ) = L & = F 3 (melting rate, a , C, V) (3.5) 



ir(V) = V = G 2 (L A , L A , I, I, V ) (3.6) 



Using the variable interaction descriptions contained 
in Section II, the remainder of the non-linear differential 
relations may be defined as follows: 



|_(I) = i = G 1 (a) , I, V, L E , m, I q ) (3.7) 



f^v) = v = G 3 (I, L E , u , w, T M , v q ) (3.8) 



ar ( V = ^p = G 4 (I ' V V w ' m ' V ) (3,9) 



lt (C) = b = S (L A' V ' V *' C } (3 - 10) 



_( W ) = w = p (Melting rate, u> , v) (3.11) 



60 



ft ( V = *M = F 2 (V ' lf V V) (3 * 12) 



By substituting L„ and I into the above equations 
for the melting rate and substituting for the derivative 
terms in the right hand side of the above relations, the 
final model equations can be listed below. 



I = G 1 (o) , I, V, L £/ m, I Q ) (3.13) 



V = G 2 [F 3 (I,L E ,aj p ,C,V) ,1^,1^(0) , I, V,L E ,m) ,V q ] (3.14) 



v = G 3 (I, L E , U) , w, T M/ v q ) (3.15) 



oo = G„(I, L_, oj , w, m, oj ) (3.16) 

p 4 E p po 



C = G 5 [L A ,V,P 3 (I,L E/ aj ,C f V) ,G 2 [F 3 (I,L E ,a3 ,C,V), 



L^I/G-^ai ,I,V,L E ,m) ] ,C q ] (3.17) 



w = F. (L , I, a) , v) (3.18) 

IE p 



61 



T M = F 2 (V/ I ' V V) (3.19) 



L A = F 3 (L E / X ' V C ' V) (3.20) 



L E = F 4 (L E' Z ' %' C) (3.21) 



The output vector becomes: 



Y l = C l ( ^ = w (3.22) 



Y 2 = c 2 (X) = T M (3.23) 



Y 3 " C 3 (X) = L A (3.24) 



Y 4 = C 4 (X) = L E (3.25) 



Y 5 = 05(1, uj L E ) = m (3.26) 



The above relations define the ninth order model and 
the output. These relationships are diagramed finally in 
Figure 3-1. 

62 



CO 
CO 
CD 

o 



u 
& 

C 
•H 

rH 

s 



u 
o 

•H 

-p 
td 

e 
o 
-p 

< 

< 

u 
o 



CD 

I 

CD 
iH 

43 
rd 

•H 

> 

CD 
-U 
ITJ 
-P 
02 



I 

en 



D 

M 




rtj 



w 



■P 

+j 

O 



XI 



<c 



1 -p 

03 



XI 



3 U 



O 



o 

-p 

c 



u 



(D 

> 
•H 
-P 
03 

rH 

3 
CU 
■H 

C 
<T3 
5* 




«l 



63 



IV. RECOMMENDATIONS AND CONCLUSIONS 

The model developed in the previous section is the 
first step in the implementation of an optimal control 
scheme for the GMA welding process. The following steps 
must be taken prior to the development, design and construc- 
tion of an actual automatic welder using this model 
formulation: 

•The nature of the vector functions F(X,U) , 
G(Y/U/R) and C(X,U) must be determined. 

•Power supply characteristics must be known 
exactly. Preferably, a power supply capable of 
operating anywhere on the V-I plane such as the 
one described by Schaper (reference 30) will be 
used. 

•Sensors accurate enough to monitor the process 
variables must be developed. 

•Values for the proper settings of the parameters 
in R must be determined for each application. 

•Correct choices of parameters such as electrode 
diameter, electrode composition, joint alignment 
tolerance and shielding gas composition must be 
specified for each application. 



64 



The nature of some of the functional relations has 
been addressed in the literature but for the most part, 
only a few of the variables have been considered in the 
equation formulation. Little is known how such variables 
as pulsing frequency, metal temperature and weldment puddle 
width affect the exact form of the equation formulation. 
Secondly, even if the form of the equations does not vary 
with changes of metal being welding, electrode diameter, 
electrode composition and shielding gas, the constants and 
values of any exponents probably do. The first derivation 
for these equations should be accomplished considering steel 
plate welding in Argon gas shielding because this situation 
is the most prevalent for industrial needs. 

The power supply characteristics will specify the 
operating regions available for the welding process; the 
power supply will be required to provide the current, 
voltage and pulsing characteristics as requested by the 
control scheme and is therefore the most important 
component in the process. Every effort should be made 
to make the power supply as adaptable as possible. 

The settings of R have been researched extensively 
and are fairly well defined. R will vary with the choice 
of all parameters and will determine the operating set 
points of the process. 



65 



Likewise, parameter requirements have been fairly 
well defined in the literature for many GMA applications. 
These requirements are based on extensive empirical data. 

Existing sensors have been discussed and, with the 
recent developments in electronic and video technology, 
should be available for welding control. These sensors 
should be able to provide sufficient accuracy. 

Automatic control of the GMA process will entail 
a very complicated implementation procedure. Presented 
here has been a description of all identifiable process 
variables and a mathematical method of bookkeeping to define 
the variable interraction using modern control theory 
techniques. 

In the following section, comments will be made 
regarding the implementation of this control scheme to 
welding. These comments are based on the authors ' s 
observations, research and experience and in some cases 
have not been documented by existing scientific evidence 
and fact. 



66 



V. COMMENTS REGARDING AUTOMATIC WELDING 

The most important variable in the GMA welding process 
when using DCRP Polarity appears to be the current; both 
current magnitude and the frequency of the pulsing determine 
the majority of the physical mechanisms which occur. Current 
should be maintained so that the transition current is ex- 
ceeded and axial spray transfer is maintained. Current and 
pulsing determine the melting rate of the electrode and 
affect the amount of heat energy that is applied to the 
weldment. 

Therefore, the time rate of change of the current 
should be determined by measurements (and hence be a 
function) of most of the variables in the process. To do 
this, the power supply has to be adaptable enough to operate 
in the desired region. Schaper (reference 30) indicated that 
the quality of the weldments that he produced improved 
greatly once he shifted power supplies from a constant- 
voltage type to one that provided constant-current 
characteristics also. 

If the current can be maintained in the region that 
insures axial spray transfer, the arc stability will increase 
and the mechanical properties of the weldment should be of 
good quality and should be reproducable. 



67 



Initial implementation of variable control for auto- 
matic welding will probably be completed on a model that is 
much simpler than the one given in Section III or on a model 
of the GTA process. The GTA process would be simpler to 
model because wire feed rate, melting rate and metal transfer 
rates can be ignored. 

If the GMA process is chosen, one or two of the state 
variables (weldment puddle width and metal temperature) that 
are not easily measured can be deleted and then model 
development can proceed. Once computer model simulations 
have been completed, testing on actual welding machines 
would be advisable. After this first step is completed, 
other variables can be added to the model. Eventually one 
or two stochastic variables (light and noise emissions) 
should be added because they probably contain much useful 
information. These stochastic variables should unlock much 
of the mystery of the dynamic characteristic of the arc 
itself. Likewise, magnetic sensing and control of the arc 
will furnish more information about the arc mechanism. 

Two different types of automatic welders are envisioned 
for the future. One type would be a large scale production 
machine that is controlled automatically in the joint path 
and is manipulated with little or no human intervention. 
This machine would probably be very large, very heavy, and 



68 



very expensive. However, another type of machine can be 
envisioned that could overcome the man-machine interface 
problem and use the manual skills as an integral part of 
the control scheme. This machine would be much smaller 
(maybe even portable) and less costly and would have uses 
such as repairs and on-site production. 

Because manual manipulative skills and human sensor 
(eyes, ears, touch) capabilities are difficult to duplicate 
by machines, it seems tragic to eliminate them from welding. 
A human operator could ensure that the torch followed the 
joint path. The human operator could vary the traverse 
speed; even such a simple system as three "idiot" lights 
meaning "speed up," "slow down," and "OK" may be sufficient 
to control traverse speed. The machine could then mani- 
pulate other variables in the process. Welder training costs 
and training time would be reduced substantially while 
weldment quality would increase. As Houldcroft indicates 
in his article, the welder could be compared to the pilot 
in a commercial airline; the machine functions as an 
extension of the operator to greatly improve the overall 
system performance. 

State variable controlled automatic welding has 

enormous potential to improve the fabrication capabilities 

of industry worldwide. As structural designs have become 

more complex and intricate, designers have demanded more 

efficient joining methods. The development of a variable 

controlled automatic welder should help to fulfill this need. 

69 



REFERENCES 



1. B & K Instruments, Inc., Technical Review to Advance 

Techniques in Acoustical, Electrical and Mechanical 
Measurement. Vol. 1, 1978, Denmark. 



2. Barkow, A.G., "Recent Advances in the Field of Automatic 
Welding of Pipeline Girth Welds in the USA, " 
Proceedings of the Pipe Welding Conference 10-13 
November 1969 , Paper 13. The Welding Institute, 
Abington, England, 1970. 



3. Bennett, A. P., "The Interaction of Material Variability 
upon Process Requirements in Automatic Control," 
Advances in Welding Processes , Third International 
Conference, 7-9 May 1974, Paper 3. The Welding 
Institute, Abington, England, 1974. 



4. Bryson, A.E. , Jr. and Y. Ho, Applied Optimal Control . 
New York, 1975. 



5. Danhier, F.G., "Automatic Orbital Welding on Pipework 
and Pipelines," Proceedings of the Pipe Welding 
Conference 10-13 November 1969 , Paper 3. The 
Welding Institute, Abington, England, 1970. 



6. Downey, J.C., D.H. Hood and D.D. Keiser, "Shrinkage in 
Mechanized Welded 16-inch Stainless Pipe," 
Welding Journal , Vol. 54, No. 3, (March 1975), 
pp. 170-175. 



7. Evans, R.M. , Automation in Welding . Metals and Ceramics 
Information Center, Report No. MC1C-74-24. 
Battelle, Columbus, Ohio. October, 1974. 



8. Gavrilov, A.N. , Automation and Mechanization of Production 
Processes in the Instrument Industry , trans. L. 
Herdon. Oxford, England, 1967. 



70 



9. Glickstein, S.S. and W. Yeniscavich, "A Review of Minor 
Element Effects on the Welding Arc and Weld 
Penetration," Welding Research Council Bulletin , 
No. 226, May, 1977. 



10. Hedrick, J.K., "2.152 Modern Control Theory and Applica- 
tions; Course Notes," MIT, Spring 1977. 
(unpublished) 



11. Houldcroft, Peter T. , "1977 Comfort A. Adams Lecture: 
Developing Precision Assembly by Welding," 
Welding Journal , Vol. 56, No. 8, (August 1977), 
pp. 15-25. 



12. Hosier, William, "Arc-Welding Times Slashed by Robot at 
Arc Research," Welding Journal , Vol. 58, No. 1, 
(January 1979), pp. 26-29. 



13. Interview with Jose Converti, Ph.D. Candidate, Mechanical 
Engineering Department, MIT, 7 December 1978. 



14. Jones, R.L., D.K. Kiltau and K.P. Havik, "Automatic 
Welding of Marine Pipelines on the Semac I Lay 
Barge," Welding Journal , Vol. 57, No. 9, 
(September 1978), pp. 15-21. 



15. Karnopp, D. and R. Rosenberg, System Dynamics: A Unified 
Approach, New York, 1975. 



16. Kwakernaak, H. and R. Sivan, Linear Optimal Control 
Systems, New York, 1972. 



17. Lesnewich, A., "MIG Welding with Pulsed Power," Welding 
Research Council Bulletin, No. 170, February, 1972 



18. Masubuchi, K. , "13.17J Welding Engineering: Course Notes," 
MIT, Fall, 1978. (unpublished) 



71 



19. Masubuchi, K. , H.M. Paynter, and F. Van Dyck, "Preliminary 
Research Proposal on 'Integrated Pipeline Manufactur- 
ing System, ' " Laboratory for Manufacturing and 
Productivity, MIT, Cambridge, Mass. (Unpublished 
paper) 



20. Masubuchi, K. , H.M. Paynter, and F. Van Dyck, "Preliminary 
Research Proposal on ' Integrated Pipeline Manufactur- 
ing System,' Second draft," Laboratory for 
Manufacturing and Productivity, MIT, Cambridge, 
Mass. (Unfinished Draft) 



21. Mills, G.S., "Fundamental Mechanisms of Penetration in 
GTA Welding," Welding Journal , Vol. 58, No. 1, 
(January 1979) , pp. 21-s - 24-s. 



22. Morris, G.J. and R.L. Apps, "Programmed Control of MIG 

Welding Parameters," Welding and Metal Fabrication , 
Vol. 45, No. 8, (October 1977), pp. 487-492. 



23. Morris, G.J. and J. Lowery, "How Electronically Controlled 

MIG can cut Costs," Welding and Metal Fabrication , 
Vol. 46, No. 10, (December 1978) , pp. 687-6§9. 

24. Murray, D.M., Applications of Aerospace Technology in 

Industry. A Technology Transfer Profile: Welding. 
Technology Management Group, ABT Associates, Inc. , 
Cambridge, Mass. September 1971. 



25. "Narrow-Gap Welding Nears the Shipyards," Welding Design 
and Fabrication, (March 1979) , pp. 118-120. 



26. Ogata, K. , Modern Control Engineering , Englewood Cliffs, 
New Jersey, 1970. 



27. Paton, E.O., "Welding in the USSR: Technology and 
Production," Welding and Metal Fabrication , 
Vol. 46, No. 3, (April 1978), pp. 201-207. 



72 



28. Phillips, Arthur L. , Current Welding Processes , ed. 

A.L. Phillips. New York, 1964. 

29. Quigley, M.B.C., "Physics of the Welding Arc," Welding and 

Metal Fabrication , Vol. 45, No. 10, (December 1977) , 
pp. 619-626. 



30. Schaper, V.D., Development of an Automated Narrow Gap 
Welder for High Strength Steel Plate , David W. 
Taylor Research and Development Center, Report No. 
MAT-76-86. Bethesda, Maryland, August 1977. 



31. Schultz, D.G. and J.L. Melsa, State Functions and Linear 
Control Systems, New York, 1967. 



32. Smith, C.J. , "Self-Adaptive Control of Penetration in a 

Tungsten Inert Gas Weld, " Advances in Welding 
Processes , Third International Conference, 7-9 
May 1974, Paper 41. The Welding Institute, 
Abington, England, 1974. 

33. Troyer, Wade E., "Programming and Pulsing the Tungsten 

Arc," Metal Progress , Vol. 197, No. 3, (March 
1975) , pp. 99-106. 



34. Vroman, A.R. and H. Brandt, "Feedback Control of GTA 

Welding Using Puddle Width Measurement, " Welding 
Journal , Vol. 55, No. 9, (September 1976), pp. 
742-749. 



35. Wilson, A.J. and D.T. Northcote, "The Development of 
Semi- Automatic Welding of Pipe in Australia," 
Proceedings of the Pipe Welding Conference 
10-13 November 1969 , Paper 34. The Welding 
Institute, Abington, England, 1970. 



73 



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